aerosol air mass type mapping over urban areas from space...
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Aerosol Air Mass Type Mapping Over Urban Areas From Space-based Multi-angle Imaging
Ralph Kahn NASA Goddard Space Flight Center
Patadia, Kahn et al. 2013
Mexico City– MISR Research Aerosol Retrieval – March 06, 2006
Eyjafjalljökull Volcano Ash Plume
MISR Standard Aerosol Retrieval, 19 April 2010
MISR Team, JPL and GSFC
The NASA Earth Observing System’s Terra Satellite
ASTER
First Light: February 24, 2000
MODIS
CERES MISR
MOPITT
Source: Terra Project Office / NASA Goddard Space Flight Center
Multi-angle Imaging SpectroRadiometer
• Nine CCD push-broom cameras
• Nine view angles at Earth surface: 70.5º forward to 70.5º aft
• Four spectral bands at each angle: 446, 558, 672, 866 nm
• Studies Aerosols, Clouds, & Surface
http://www-misr.jpl.nasa.gov http://eosweb.larc.nasa.gov
Ten Years of Seasonally Averaged Mid-visible Aerosol Optical Depth from MISR
…includes bright desert dust source regions MISR Team, JPL and GSFC
2000 2004 2003 2002 2001 2005 2006 2007
Dec-Feb
Mar-May
Jun-Aug
Sep-Nov
2008 2009
Over-ocean regression coefficient 0.90 Regression line slope 0.75
MODIS QC ≥ 1
Over-land regression coefficient 0.71 Regression line slope 0.60
MODIS QC = 3 Kahn, Nelson, Garay et al., TGARS 2009
MISR = 0.09 + 0.60 x MODIS Correlation Coeff = 0.713 Std Dev (MISR-MODIS) = 0.117
Land Ocean MISR = 0.04 + 0.75 x MODIS Correlation Coeff = 0.902 Std Dev (MISR-MODIS) = 0.041
MISR-MODIS Coincident AOT Outlier Clusters
Dark Blue [MISR > MODIS] – N. Africa Mixed Dust & Smoke Cyan [MODIS > MISR, AOD large] – Indo-Gangetic Plain Dark Pollution Aerosol Green [MODIS >> MISR] – Patagonia and N. Australia MODIS Unscreened Bright Surface
Kahn et al., TGARS 2009
Smoke from Mexico -- 02 May 2002
0.0 1.2 -.25 3.0 0.0 1.0
Aerosol: Amount Size Shape
Medium Spherical Smoke Particles
Dust blowing off the Sahara Desert -- 6 February 2004
Large Non-Spherical Dust Particles
0.0 1.2 -.25 3.0 0.0 1.0
Kahn et al., JGR 2001
With current technology, we are aiming for Regional-to-Global Aerosol Type Discrimination something like this…
Global, Monthly Aerosol Maps Based on Expected MISR Sensitivity
The examples shown here are simulated from aerosol transport model calculations…
5 Groupings Based on Aerosol Properties 13 Groupings Based on Aerosol Properties
• With MISR – About a dozen Aerosol Air Mass type distinctions, based on 3-5 size bins, 2-4 bins based on SSA, and spherical vs. non • Sensitivity depends on conditions; AOD >~0.15 needed, etc.
Adding NIR & UV wavelengths, Polarization should increase this capability
MISR Aerosol Type Distribution
Spherical Non-Absorbing
Spherical Absorbing Non-Spherical
Kahn, Gaitley, Garay, et al., JGR 2010
Ouarzazate (30.93, -6.91)
AOT(558) ~0.30-0.45
Tinfou (30.23, -5.61)
AOT(558) ~0.45-0.55
0.3
0.35
0.4
0.45
0.5
6:00 8:00 10:00 12:00 14:00
Spec
tral
Opt
ical
Dep
th
Time (UTC)
AOT (440 nm)AOT (500 nm)AOT (675 nm)
MISR AOT(558) ~ 0.30-0.45
Ouarzazate AERONET A. Ansmann
0.4
0.5
0.6
0.7
0.8
0.9
6:00 8:00 10:00 12:00 14:00
Spec
tral
Opt
ical
Dep
th
Time (UTC)
AOT (440 nm)AOT (500 nm)AOT (670 nm)
Tinfou Sun Photometer W. von Hoyningen-Huene & T. Dinter
MISR AOT(558) ~ 0.45-0.55
Falcon HSRL
SAMUM Campaign Morocco – June 04, 2006
MISR SAMUM Aerosol Air Masses (V19) - June 04, 2006 Orbit 34369, Path 201, Blocks 65-68, 11:11 UTC
0.0 1.0 0.5
Ouarzazate AOT(558) ~0.30-0.45
Tinfou AOT(558) ~0.45-0.55
(27.9, -5.6)
a
1.5 0.9 0.0
Ouarzazate ANG ~0.5-0.6
Tinfou ANG ~0.1-0.7
(30.1, -6.4)
b
0.94 0.97 1.0
Ouarzazate SSA(558) ~0.95-0.99
Tinfou SSA(558) ~0.99-1.0
c
0.3 1.0 0.7
Ouarzazate FrSph ~0.4-0.6
Tinfou FrSph ~0.6-0.8
d
Kahn et al., Tellus 2009
• A dust-laden density flow in the SE corner of the MISR swath • High SSA, ANG & Fraction Spherical region SE of Ouarzazate, includes Zagora
1 1
2
2
2 2
1 3
3
3
3 1
Kahn et al., Tellus 2009
MISR SAMUM Aerosol Air Mass Validation - June 04, 2006
Falcon F-20 HSRL - Thin layers of small, bright particles
NOAA/HYSPLIT Back Trajectories -Source in N Algeria for 2, 3 but not 1.
1
2
3
4
5
1
2
3
4
5
0.0 0.6 1.2 0.0 1.2 2.4 0 5000 10,000
Oregon Fire Sept 04 2003 Orbit 19753 Blks 53-55 MISR Aerosols V17, Heights V13 (no winds)
Kahn, et al., JGR 2007
0
2
4
6
8
10
0 2 4 6 8 10
Atmospheric Stabilit
P 1-2P 4-5
0 50 100
0.5
1.5
2.5
3.5
4.5
5.5
6.5
7.5
8.5
9.5
Hei
ght (
km)
Number of Pixels
P1
0 100 200
0.5
1.5
2.5
3.5
4.5
5.5
6.5
7.5
8.5
9.5
Hei
ght (
km)
Number of Pixels
P2
0 50 100
0.5
1.5
2.5
3.5
4.5
5.5
6.5
7.5
8.5
9.5
Hei
ght (
km)
Number of Pixels
P3
0 50 100
0.5
1.5
2.5
3.5
4.5
5.5
6.5
7.5
8.5
9.5H
eigh
t (km
)
Number of Pixels
P4
0 20 40 60
0.5
1.5
2.5
3.5
4.5
5.5
6.5
7.5
8.5
9.5
Hei
ght (
km)
Number of Pixels
P5
Wildfire Smoke Injection Heights & Source Strengths [These are the two key parameters representing aerosol sources in climate models]
MISR Stereo Heights:
~3400 Smoke Plumes Over N. America
% of Plumes injected above boundary layer stratified by vegetation type & year
Val Martin et al. ACP 2010
MODIS Smoke Plume Image & Aerosol Amount Snapshots
GoCART Model-Simulated Aerosol Amount Snapshots for Different Assumed Source Strengths Petrenko et al., JGR 2012
Different Techniques for Assuming Model Source Strength Overestimate or Underestimate Observation
Systematically in Different Regions
Kahn & Limbacher, ACP 2012
Volcanic Plume Properties: Height, Particle Size, Shape, Brightness MISR Observations – Iceland Volcano Eruption 07 May 2010
Plume Particles vs. Background: Larger, darker, more non-spherical, much more abundant; Brighten & decrease in size downwind
Plume Height
January 2007 July 2007
Mean Best Estimate AOD Map & Histogram Distribution
Sahara
Most Frequent Lowest Residual Aerosol Type Mixture Group, Stratified by AOD
Number of Successful Mixtures vs. Normalized AOD & vs. Normalized Scattering Angle Range
AOD < 0.2 AOD ≥ 0.2 AOD ≥ 0.2 AOD < 0.2
Histograms of Lowest Residual & All Successful Aerosol Type Mixture Groups
MISR Aerosol V22 Algorithm Upgrade Priorities Supporting Dust, Smoke, & Aerosol Pollution Applications
• Based on 10 Years of Validation Data
-- Low-light-level gap & quantization noise
-- High-AOD underestimation of AOD (missing low-SSA particles; algorithm issues)
-- Missing Medium-mode particles (reff ~ 0.57, 1.28 µm)
-- More spherical, absorbing particles (SSA ~ 0.94, 0.84, maybe 0.74)
-- Mixtures of smoke & dust analogs; more Bi- and Tri-modal spherical mixtures
-- Flag indicating when there is insufficient sensitivity for particle property retrieval (possibly different retrieval path under this condition)
-- Lack of a good Coarse-mode Dust Optical Analog remains an issue
Kahn, Gaitley, Garay, et al., JGR 2010
• Need to isolate Near-surface Aerosol Component
• Need sufficient Spatial-Temporal Coverage to capture Severe Events
• Detailed Chemical Speciation often required
• High Spatial Resolution often required (e.g., in Urban areas)
Improving Air Quality Models
Zhang et al., GRL. 2007 Surface-based mass-spec aerosol composition measurements
Recent efforts use models to parse satellite column AOD; speciate spherical particle fraction [Y. Liu et al. JAWMA 2007; Martin and von Donkellar, 2008]
Pollution Aerosol Concentrated in Ganges Valley near Kanpur, India (MISR)
DiGirolamo et al., GRL, 2004
MISR mid-visible AOD [Winter, 2001-2004; white --> AOD >0.6]
NCEP Winds + Topography [Black=surface; Red=850 mb;
contours=vertical, solid=subsidence]
Air Quality: BL Aerosol Concentration [MISR + MODIS] AOD & GEOS-Chem Vertical Distribution
Van Donkelaar et al., Environ. Health Prespect. 2010
[BL PM2.5] / [Total-col. AOD]
2001- 2006
Derived PM2.5
MISR - GEOS-Chem Regression Model To Map Near-surface Aerosol Pollution
Y. Liu et al, JAWMA 2007
• Using MISR Particle Shape as well as AOT to constrain model --> much better result • Will add column Size and SSA information when MISR retrieval is more robust
MISR / GEOS-CHEM Retrieval Surface network (IMPROVE) measurements
-Con
stra
ined
Mod
el
Eastern US
Western US
EPA Surface Measurements
PM2.5 SO4
Characterizing seasonal changes in anthropogenic and natural aerosols w.r.t. preceding season over the Indian Subcontinent
Winter (Dec-Feb) Monsoon (Jun-Sep) Post-monsoon (Oct-Nov) Pre-monsoon (Mar-May)
Dey & Di Girolamo JGR 2010
Pre-monsoon influx of dust from the Great Indian Desert and Arabian Peninsula
Large influence of anthropogenic particles
due to pre-monsoon biomass burning
Additional influence of maritime particles
produced by high surface wind
Large influence of anthropogenic particles due to seasonal peak in biomass burning and reduced dust
transport
Increased wintertime transport of
anthropogenic pollution
fNatural fAnthro.
Index
Reduced dust loading due to
monsoon precipitation
Himalayan foothills - advection of
anthropogenic particles from Indo-
Gangetic Basin
Index uses MISR-retrieved particle shape and size constraints to separate natural from anthorpogenic aerosol
70˚aft Nadir
Nadir
Mexico City INTEX-B/MILAGRO MISR March 06, 2006 Orb 33062 Path 26 Block 75
Patadia et al.
Mapping AOD & Aerosol Air-Mass-Type in Urban Regions
Urban Pollution AOD & Aerosol Air Mass Type Mapping INTEX-B, 06 & 15 March 2006
Patadia et al., JGR submitted
AOD Fr. Non-Sph. ANG SSA
March 06
March 15
Aerosol Air Masses: Dust (non-spherical), Smoke (spherical, spectrally steep absorbing), and Pollution particles (spherical, spectrally flat absorbing) dominate specific regions
Zhang & Reid, ACP 2010
MODIS10-Year Global/Regional Over-Water AOD Trends
• Statistically negligible (±0.003/decade) global-average over-water AOD trend • Statistically significant increases over the Bay of Bengal, E. Asia coast, Arabian Sea
Trend
Statistical Significance
Key Attributes of the MISR Version 22 Aerosol Product
• AOT Coverage – Global but limited sampling on a monthly basis
• AOT Accuracy – Maintained even when particle property information is poor
• Particle Size – 2-3 groupings reliably; quantitative results vary w/conditions
• Particle Shape – spherical vs. non-spherical robust, except for coarse dust
• Particle SSA – useful for qualitative distinctions
• Aerosol Type Information – diminished when AOT < 0.15 or 0.2
• Particle Property Retrievals – improvement expected w/algorithm upgrades
• Aerosol Air-mass Types – more robust than individual properties
PLEASE READ THE QUALITY STATEMENT!!!
… and more details are in publications referenced therein
Satellites
Model Validation • Parameterizations • Climate Sensitivity • Underlying mechanisms
CURRENT STATE • Initial Conditions • Assimilation
Remote-sensing Analysis • Retrieval Validation • Assumption Refinement
frequent, global snapshots;
aerosol amount & aerosol type maps,
plume & layer heights
space-time interpolation, DARF &
Anthropogenic Component
calculation and prediction
Suborbital
targeted chemical & microphysical detail
point-location time series
Regional Context
Kahn, Survy. Geophys. 2012
Aerosol-type Predictions
Future Mission Possibilities… AirMSPI
Bakersfield CA 18 January 2013 (+47.5° View)
SAM-CAAM [Systematic Aircraft Measurements
to Characterize Aerosol Air Masses]
Primary Objectives:
• Interpreting and enhancing satellite aerosol-type retrieval products
• Characterizing statistically particle properties for the major aerosol types, providing detail unobtainable from space, but needed to improve:
-- Satellite aerosol retrieval algorithms
-- The translation between satellite-retrieved aerosol optical properties and species-specific aerosol mass and size